Time-series prediction based on double pyramid bidirectional feature fusion mechanism

抄録

The application of time series prediction is very extensive, and it is an important problem across many fields, such as stock prediction, sales prediction and loan prediction and so on, which play a great value in production and life. It requires that the model can effectively capture the long term feature dependence between the output and input. Recent studies show that Transformer can improve the prediction ability of time series. However, Transformer has some problems that make it unable to be directly applied to time series prediction, such as: (1) Local agnosticism: Self attention in Transformer is not sensitive to short term feature dependence, which leads to model anomalies in time series; (2) Memory bottleneck: The spatial complexity of regular transformation increases twice with the sequence length, making direct modeling of long time series infeasible. In order to solve these problems, this paper designs an efficient model for long time series prediction. It is a double pyramid bidirectional feature fusion mechanism network with parallel Temporal Convolution Network (TCN) and FastFormer. This network structure can combine the time series fine grained information captured by the Temporal Convolution Network with the global interactive information captured by FastFormer, it can well handle the time series prediction problem.